Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » synthetic_cuboid » Best Parameters
Navigation:
027Hints:

Which parameter sets lead to the optimal clustering quality?

Please choose a clustering quality measure:
Program Best quality Parameter set Clustering
CLARA 1.0 metric=euclidean
k=57
samples=20		 Clustering
Self Organizing Maps 1.0 x=2
y=9		 Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=10
dc=1.3582306799958523		 Clustering
HDBSCAN 1.0 minPts=226
k=250		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=204		 Clustering
c-Means 1.0 k=37
m=1.01		 Clustering
k-Medoids (PAM) 1.0 k=249 Clustering
DIANA 1.0 metric=euclidean
k=126		 Clustering
DBSCAN 1.0 eps=1.4104703215341545
MinPts=208		 Clustering
Hierarchical Clustering 1.0 method=complete
k=43		 Clustering
fanny 1.0 k=14
membexp=2.0		 Clustering
k-Means 1.0 k=63
nstart=10		 Clustering
DensityCut 1.0 alpha=0.012202380952380952
K=30		 Clustering
clusterONE 0.0 s=75
d=0.06666666666666667		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.3917973115372651
maxits=2750
convits=500		 Clustering
Markov Clustering 0.0 I=2.3561561561561564 Clustering
Transitivity Clustering 1.0 T=1.2095124212021275 Clustering
MCODE 1.0 v=0.4
cutoff=1.3059910384575504
haircut=F
fluff=F		 Clustering